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The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international pan-cancer registry with the goal to inform cancer research and clinical care worldwide. Founded in late 2015, the milestone GENIE 9.1-public release contains data from >110,000 tumors from >100,000 people treated at 19 cancer centers from the United States, Canada, the United Kingdom, France, the Netherlands, and Spain. Here, we demonstrate the use of these real-world data, harmonized through a centralized data resource, to accurately predict enrollment on genome-guided trials, discover driver alterations in rare tumors, and identify cancer types without actionable mutations that could benefit from comprehensive genomic analysis. The extensible data infrastructure and governance framework support additional deep patient phenotyping through biopharmaceutical collaborations and expansion to include new data types such as cell-free DNA sequencing. AACR Project GENIE continues to serve a global precision medicine knowledge base of increasing impact to inform clinical decision-making and bring together cancer researchers internationally.Significance:AACR Project GENIE has now accrued data from >110,000 tumors, placing it among the largest repository of publicly available, clinically annotated genomic data in the world. GENIE has emerged as a powerful resource to evaluate genome-guided clinical trial design, uncover drivers of cancer subtypes, and inform real-world use of genomic data.This article is highlighted in the In This Issue feature, p. 2007

Supplementary Table from AACR Project GENIE: 100,000 Cases and Beyond

Supplementary Figure from AACR Project GENIE: 100,000 Cases and Beyond

PurposeMonitoring levels of circulating tumor-derived DNA (ctDNA) represents a non-invasive snapshot of tumor burden and potentially clonal evolution. Here we describe how a novel statistical model that uses serial ctDNA measurements from shallow whole genome sequencing (sWGS) in metastatic breast cancer patients produces a rapid and inexpensive assessment that is predictive of treatment response and progression-free survival.Patients and MethodsA cohort of 188 metastatic breast cancer patients had DNA extracted from serial plasma samples (total 1098, median=4, mean=5.87). Plasma DNA was assessed using sWGS and the tumor fraction in total cell free DNA estimated using ichorCNA. This approach was compared with ctDNA targeted sequencing and serial CA 15-3 measurements. The longitudinal ichorCNA values were used to develop a Bayesian learning model to predict subsequent treatment response.ResultsWe identified a transition point of 7% estimated tumor fraction to stratify patients into different categories of progression risk using ichorCNA estimates and a time-dependent Cox model, validated across different breast cancer subtypes and treatments, outperforming the alternative methods. We then developed a Bayesian learning model to predict subsequent treatment response with a sensitivity of 0.75 and a specificity of 0.66.ConclusionIn patients with metastatic breast cancer, sWGS of ctDNA and ichorCNA provide prognostic and predictive real-time valuable information on treatment response across subtypes and therapies. A prospective large-scale clinical trial to evaluate clinical benefit of early treatment changes based on ctDNA levels is now warranted.

The biology of breast cancer response to neoadjuvant therapy is underrepresented in the literature and provides a window-of-opportunity to explore the genomic and microenvironment modulation of tumours exposed to therapy. Here, we characterised the mutational, gene expression, pathway enrichment and tumour-infiltrating lymphocytes (TILs) dynamics across different timepoints of 35 HER2-negative primary breast cancer patients receiving neoadjuvant eribulin therapy (SOLTI-1007 NEOERIBULIN-NCT01669252). Whole-exome data (N = 88 samples) generated mutational profiles and candidate neoantigens and were analysed along with RNA-Nanostring 545-gene expression (N = 96 samples) and stromal TILs (N = 105 samples). Tumour mutation burden varied across patients at baseline but not across the sampling timepoints for each patient. Mutational signatures were not always conserved across tumours. There was a trend towards higher odds of response and less hazard to relapse when the percentage of subclonal mutations was low, suggesting that more homogenous tumours might have better responses to neoadjuvant therapy. Few driver mutations (5.1%) generated putative neoantigens. Mutation and neoantigen load were positively correlated (R2 = 0.94, p = R2 = 0.16, p = 0.02). An enrichment in pathways linked to immune infiltration and reduced programmed cell death expression were seen after 12 weeks of eribulin in good responders. VEGF was downregulated over time in the good responder group and FABP5, an inductor of epithelial mesenchymal transition (EMT), was upregulated in cases that recurred (p

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Importantly, ENLIGHT-DeepPT successfully predicts true responders in five independent patients’ cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, trained and tested on the same cohort in cross validation. Its future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.Statement of SignificanceENLIGHT-DeepPT is the first approach shown to successfully predict response tomultipletargeted and immune cancer therapies from H&E slides. In distinction from all previous H&E slides prediction approaches, it does not require supervised training on a specific cohort for each drug/indication treatment but is trained to predict expression on the TCGA cohort and then can predict response to an array of treatments without any further training. ENLIGHT-DeepPT can provide rapid treatment recommendations to oncologists and help advance precision oncology in underserved regions and low-income countries.

Background: Previous studies have independently validated the prognostic relevance of residual cancer burden (RCB) after neoadjuvant chemotherapy. We used results from several independent cohorts in a pooled patient-level analysis to evaluate the relationship of RCB with long-term prognosis across different phenotypic subtypes of breast cancer, to assess generalisability in a broad range of practice settings. Methods: In this pooled analysis, 12 institutes and trials in Europe and the USA were identified by personal communications with site investigators. We obtained participant-level RCB results, and data on clinical and pathological stage, tumour subtype and grade, and treatment and follow-up in November, 2019, from patients (aged ≥18 years) with primary stage I–III breast cancer treated with neoadjuvant chemotherapy followed by surgery. We assessed the association between the continuous RCB score and the primary study outcome, event-free survival, using mixed-effects Cox models with the incorporation of random RCB and cohort effects to account for between-study heterogeneity, and stratification to account for differences in baseline hazard across cancer subtypes defined by hormone receptor status and HER2 status. The association was further evaluated within each breast cancer subtype in multivariable analyses incorporating random RCB and cohort effects and adjustments for age and pretreatment clinical T category, nodal status, and tumour grade. Kaplan-Meier estimates of event-free survival at 3, 5, and 10 years were computed for each RCB class within each subtype. Findings: We analysed participant-level data from 5161 patients treated with neoadjuvant chemotherapy between Sept 12, 1994, and Feb 11, 2019. Median age was 49 years (IQR 20–80). 1164 event-free survival events occurred during follow-up (median follow-up 56 months [IQR 0–186]). RCB score was prognostic within each breast cancer subtype, with higher RCB score significantly associated with worse event-free survival. The univariable hazard ratio (HR) associated with one unit increase in RCB ranged from 1·55 (95% CI 1·41–1·71) for hormone receptor-positive, HER2-negative patients to 2·16 (1·79–2·61) for the hormone receptor-negative, HER2-positive group (with or without HER2-targeted therapy; p

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers., Integration of pre-treatment tumour features in predictive models using machine learning could inform on response to therapy.

The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international pan-cancer registry with the goal to inform cancer research and clinical care worldwide. Founded in late 2015, the milestone GENIE 9.1-public release contains data from >110,000 tumors from >100,000 people treated at 19 cancer centers from the United States, Canada, the United Kingdom, France, the Netherlands, and Spain. Here, we demonstrate the use of these real-world data, harmonized through a centralized data resource, to accurately predict enrollment on genome-guided trials, discover driver alterations in rare tumors, and identify cancer types without actionable mutations that could benefit from comprehensive genomic analysis. The extensible data infrastructure and governance framework support additional deep patient phenotyping through biopharmaceutical collaborations and expansion to include new data types such as cell-free DNA sequencing. AACR Project GENIE continues to serve a global precision medicine knowledge base of increasing impact to inform clinical decision-making and bring together cancer researchers internationally. Significance: AACR Project GENIE has now accrued data from >110,000 tumors, placing it among the largest repository of publicly available, clinically annotated genomic data in the world. GENIE has emerged as a powerful resource to evaluate genome-guided clinical trial design, uncover drivers of cancer subtypes, and inform real-world use of genomic data. This article is highlighted in the In This Issue feature, p. 2007

Funder: Cancer Research UK, Funder: Wellcome Trust, DNA methylation is aberrant in cancer, but the dynamics, regulatory role and clinical implications of such epigenetic changes are still poorly understood. Here, reduced representation bisulfite sequencing (RRBS) profiles of 1538 breast tumors and 244 normal breast tissues from the METABRIC cohort are reported, facilitating detailed analysis of DNA methylation within a rich context of genomic, transcriptional, and clinical data. Tumor methylation from immune and stromal signatures are deconvoluted leading to the discovery of a tumor replication-linked clock with genome-wide methylation loss in non-CpG island sites. Unexpectedly, methylation in most tumor CpG islands follows two replication-independent processes of gain (MG) or loss (ML) that we term epigenomic instability. Epigenomic instability is correlated with tumor grade and stage, TP53 mutations and poorer prognosis. After controlling for these global trans-acting trends, as well as for X-linked dosage compensation effects, cis-specific methylation and expression correlations are uncovered at hundreds of promoters and over a thousand distal elements. Some of these targeted known tumor suppressors and oncogenes. In conclusion, this study demonstrates that global epigenetic instability can erode cancer methylomes and expose them to localized methylation aberrations in-cis resulting in transcriptional changes seen in tumors.

Aim: We recently published the first machine learning framework that integrates multi-omic data derived from the pre-therapy breast tumor ecosystem to accurately predict response to neoadjuvant systemic therapy (Sammut et al, Nature 2021). We aimed to extend this framework to incorporate serially acquired multi-omic data to further improve response predictions and model tumor biology as it is perturbed by treatment. Methods: Breast tumor core biopsies were acquired at diagnosis from 168 women that went on to receive pre-operative chemotherapy (or chemotherapy plus anti-HER2 targeted therapy). Serial tumor core biopsies were obtained midway (n=78) and on completion (n=69) of neoadjuvant systemic therapy. Response was assessed at surgery using the Residual Cancer Burden score. Core biopsies were molecularly profiled by shallow whole genome, exome and RNA sequencing and their histological architecture characterized using digital pathology. Baseline clinical, molecular, and digital pathology imaging features associated with response were identified and their dynamics modelled throughout therapy. Results: At baseline, a total of 34 features derived from multi-omic data were associated with response to neoadjuvant therapy. These included: tumor mutation and neoantigen burden, subclonal diversity, HRD and chromosomal instability. A suppressed immune response, typified by the presence of T-cell dysfunction and immune exclusion, was associated with extensive chemoresistance. The changes in abundance of these features across the serially sampled on-therapy tumors were then mapped to response outcomes. An early increase in adaptive and innate immune infiltration and activation was associated with a linear decrease in expressed neoantigens, tumor proliferation and loss of subclonal mutational and copy number diversity, indicating early response to therapy. Conversely, a stable tumor and microenvironment transcriptional landscape throughout treatment, corresponding to a minimal change in tumor clonal architecture and microenvironment composition, was associated with a poor response to therapy. Notably, tumors with LOH HLA failed to engage a productive immune response during treatment and this was associated with resistance. A dynamic framework that models the change of these on-therapy features and extends the functionality of the published static machine learning model is being developed. Conclusion: Response to neoadjuvant therapy is determined by the baseline characteristics of the tumor ecosystem. During therapy, both the tumor and its microenvironment follow distinct evolutionary trajectories that can be mapped to outcome. The change in enrichment of features derived from the tumor and its microenvironment can be integrated within machine learning frameworks that leverage dynamic data from the entire therapy time course for more accurate response prediction. Citation Format: Stephen-John Sammut, Mireia Crispin-Ortuzar, Suet-Feung Chin, Elena Provenzano, Wei Cope, Ali Dariush, Sarah-Jane Dawson, Paul D. Pharoah, Florian Markowetz, Oscar M. Rueda, Helena M. Earl, Carlos Caldas. Predicting response to treatment in early breast cancer using dynamic integrative multi-omic profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 476.

Molecular profiling of breast cancer has enabled the development of more robust molecular prognostic signatures and therapeutic options for breast cancer patients. However, non-Caucasian populations remain understudied. Here, we present the mutational, transcriptional, and copy number profiles of 560 Malaysian breast tumours and a comparative analysis of breast cancers arising in Asian and Caucasian women. Compared to breast tumours in Caucasian women, we show an increased prevalence of HER2-enriched molecular subtypes and higher prevalence of TP53 somatic mutations in ER+ Asian breast tumours. We also observe elevated immune scores in Asian breast tumours, suggesting potential clinical response to immune checkpoint inhibitors. Whilst HER2-subtype and enriched immune score are associated with improved survival, presence of TP53 somatic mutations is associated with poorer survival in ER+ tumours. Taken together, these population differences unveil opportunities to improve the understanding of this disease and lay the foundation for precision medicine in different populations., Molecular profiling of breast cancer in non-Caucasian populations remains underexplored. Here the authors report a high prevalence of HER2-subtypes and enriched immune score with improved survival and higher rates of TP53 somatic mutations with poorer survival in ER+ tumours in a Malaysian cohort.

Advances in genomic technologies in the last decade have revolutionised the field of medicine, especially in cancer, by producing a large amount of genetic information, often referred to as Big Data. The identification of genetic predisposition changes, prognostic signatures, and cancer driver genes, which when mutated can act as genetic biomarkers for both targeted treatments and disease monitoring, has greatly advanced our understanding of cancer. However, there are still many challenges, such as more sophisticated analysis tools and higher processing capacity, along with cheaper storage, faster and more efficient data transfer, that must be overcome before personalised medicine finally becomes a reality.

Longitudinal analysis of circulating tumor DNA (ctDNA) has shown promise for monitoring treatment response. However, most current methods lack adequate sensitivity for residual disease detection during or after completion of treatment in patients with nonmetastatic cancer. To address this gap and to improve sensitivity for minute quantities of residual tumor DNA in plasma, we have developed targeted digital sequencing (TARDIS) for multiplexed analysis of patient-specific cancer mutations. In reference samples, by simultaneously analyzing 8 to 16 known mutations, TARDIS achieved 91 and 53% sensitivity at mutant allele fractions (AFs) of 3 in 104 and 3 in 105, respectively, with 96% specificity, using input DNA equivalent to a single tube of blood. We successfully analyzed up to 115 mutations per patient in 80 plasma samples from 33 women with stage I to III breast cancer. Before treatment, TARDIS detected ctDNA in all patients with 0.11% median AF. After completion of neoadjuvant therapy, ctDNA concentrations were lower in patients who achieved pathological complete response (pathCR) compared to patients with residual disease (median AFs, 0.003 and 0.017%, respectively, P = 0.0057, AUC = 0.83). In addition, patients with pathCR showed a larger decrease in ctDNA concentrations during neoadjuvant therapy. These results demonstrate high accuracy for assessment of molecular response and residual disease during neoadjuvant therapy using ctDNA analysis. TARDIS has achieved up to 100-fold improvement beyond the current limit of ctDNA detection using clinically relevant blood volumes, demonstrating that personalized ctDNA tracking could enable individualized clinical management of patients with cancer treated with curative intent.

Background Tumor-infiltrating lymphocytes (TILs) represent a prognostic factor for survival in primary breast cancer (BC). Nonetheless, neoepitope load and TILs cytolytic activity are modest in BC, compromising the efficacy of immune-activating antibodies, which do not yet compete against immunogenic chemotherapy. Patients and methods We analyzed by functional flow cytometry the immune dynamics of primary and metastatic axillary nodes [metastatic lymph nodes (mLN)] in early BC (EBC) after exposure to T-cell bispecific antibodies (TCB) bridging CD3e and human epidermal growth factor receptor 2 (HER2) or Carcinoembryonic Antigen-Related Cell Adhesion Molecule 5 (CEACAM5), before and after chemotherapy. Human leukocyte antigen (HLA) class I loss was assessed by whole exome sequencing and immunohistochemistry. One hundred primary BC, 64 surrounding ‘healthy tissue’ and 24 mLN-related parameters were analyzed. Results HLA loss of heterozygosity was observed in EBC, at a clonal and subclonal level and was associated with regulatory T cells and T-cell immunoglobulin and mucin-domain-3 expression restraining the immuno-stimulatory effects of neoadjuvant chemotherapy. TCB bridging CD3e and HER2 or CEACAM5 could bypass major histocompatibility complex (MHC) class I loss, partially rescuing T-cell functions in mLN. Conclusion TCB should be developed in BC to circumvent low MHC/peptide complexes.

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1–6. It is therefore essential to identify patients who have a high risk of late relapse7–9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprising about one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47–62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials. A statistical framework for breast-cancer recurrence uses long-term follow-up data and a knowledge of molecular subcategories to model distinct disease stages and to predict the risk of relapse.

Figure S1. Schematic overview of the computational pipeline to identify somatic mutations in NG-TAS data from longitudinal samples. Figure S2. Representative image of the Bioanalyser gel plot. The 8plex PCR products were analysed using Bioanalyser for primer efficiency and quality control. Figure S3. Fragment size distribution according to the Bioanalyser results for cfDNA extracted from the media where NA12878 cells were grown (main peak at around 160-170bp). Figure S4. (A) Percentage of amplicons having more than 100x coverage for 2, 5 and 10 ng of input cfDNA from NA12878 sample. (B) Percentage of reads on target for 2, 5 and 10 ng of input cfDNA from NA12878 sample. Figure S5. Detailed representation of mutations identified in tumour or plasma samples of 21 metastatic breast cancer cases. The colour gradient indicates the VAF as indicated; PT = primary tumour, M = metastasis biopsy, V1â Śn = plasma. Table S1. The proportion of NA12878 and NA11840 for the generation of the cfDNA dilution series with expected VAF. Table S2. Primers and Probes for PIK3CA and ESR1 hotspot mutations for digital PCR. (PDF 705 kb)

Background: Achieving a pathologic complete response (pCR) has been shown on the patient level to predict excellent long-term event-free survival outcomes. Residual cancer burden (RCB) quantifies the extent of residual disease for patients who did not achieve pCR. We have previously observed in the I-SPY 2 TRIAL that while metastatic events outside the central nervous system (CNS) were dramatically reduced in the setting of pCR, the incidence of CNS metastasis remained similar across RCB classes, raising the possibility that these CNS events may be independent of response in the breast. In this study, we evaluate the type and sites of recurrences by RCB in a large pooled dataset, which allows for analysis within subtype, to validate these findings. Methods: 5161 patients pooled across 12 institutions/trials with available RCB and event-free survival (EFS) data were included in this analysis. EFS was calculated as the interval between treatment initiation, and locoregional recurrence, distant recurrence or death from any cause; patients without event are censored at time of last follow-up. The median follow-up is 4.6 years. We summarized the EFS event type, further sub-dividing the distant recurrence events (DR) by their site of relapse (CNS-only, CNS and other sites, Non-CNS). We used a competing risk (Fine-Gray) model to assess which of these site-specific relapses differ between RCB classes and estimated the cumulative incidence of CNS-only and non-CNS events at 5 years. Analyses were performed across the entire study population and within HR/HER2 defined subtypes. Results: Among the 5161 subjects, there were 1164 EFS events, including 92 (7.9%) local recurrences (without distant recurrence and/or death) and 1072 distant recurrence-free survival (DRFS) events. Among the DRFS events, 158 patients died without a distant recurrence. 914 experienced distant recurrences, including 90 (9.8%) with CNS-only, 145 (15.9%) with CNS and other sites, 664 (72.6%) with non-CNS distant recurrence; 15 (1.6%) patients had missing recurrence site information. Table 1 summarizes the cumulative incidence of CNS-only and non-CNS recurrence at 5 years and the proportion of CNS-only recurrences among DR events by RCB class overall and within each HR/HER2 subtypes. The incidence of CNS-only recurrences was low and similar across RCB classes. In contrast, the incidence of non-CNS recurrences increases with increasing RCB. As a result, CNS-only recurrences are proportionally higher within the RCB-0 and RCB-I than in the RCB-II and RCB-III groups, largely because of the low DR event rate and relative low frequency of non-CNS recurrence events within the RCB-0 and RCB-I classes. Overall, 27% of the recurrences in the setting of pCR (RCB-0) are due to CNS-only recurrences.Conclusions: Consistent with previous studies, our large pooled analysis confirmed that CNS-only recurrences are uncommon but appear similar across RCB groups, independent of response, suggesting that the CNS is a treatment sanctuary site. In contrast, non-CNS recurrence rates increase as RCB increases. These findings suggest that inclusion of CNS-only recurrences as an outcome event may impact the association between neoadjuvant therapy response and long-term outcomes in the context of current therapies. Novel therapies that cross the blood brain barrier will be needed to impact CNS recurrence rates. Table 1: Cumulative Incidence of CNS Only and non-CNS Distant Recurrences at 5 years and proportion of CNS-only events among DR eventsRCB Class0IIIIIIpOverall (5161)N16766622017806Cum. Inc. CNS Only2%2%2%1%0.627Cum. Inc. Non-CNS3%6%16%27% Citation Format: Sonal Shad, Marieke van der Noordaa, Marie Osdoit, Diane de Croze, Anne-Sophie Hamy, Marick Lae, Fabien Reyal, Miguel Martin, María Del Monte-Millán, Sara López-Tarruella, I-SPY 2 TRIAL Consortium, Judy C Boughey, Matthew P Goetz, Tanya Hoskin, Rebekah Gould, Vicente Valero, Gabe Sonke, Tessa G Steenbruggen, Maartje van Seijen, Jelle Wesseling, John Bartlett, Stephen Edge, Mi-Ok Kim, Jean Abraham, Carlos Caldas, Helena Earl, Elena Provenzano, Stephen-John Sammut, David Cameron, Ashley Graham, Peter Hall, Lorna Mackintosh, Fan Fang, Andrew K Godwin, Kelsey Schwensen, Priyanka Sharma, Angela DeMichele, Janet Dunn, Louise Hiller, Larry Hayward, Jeremy Thomas, Kimberly Cole, Lajos Pusztai, Laura Van't Veer, Fraser Symmans, Laura Esserman, Christina Yau. Site of recurrence after neoadjuvant therapy: A multi-center pooled analysis [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD13-02.

Background: Accurate circulating biomarkers for detecting residual disease can help guide therapy decisions, particularly in early-stage cancer patients. However, currently available methods lack the sensitivity required to confidently assess the presence of residual disease in patients with low tumor burden. To address this need, we have developed TARDIS (Targeted Digital Sequencing), a personalized, multiplexed amplicon sequencing method capable of tracking as many as 100 or more mutations simultaneously. Methods: We obtained tumor biopsies and longitudinal plasma samples from patients with early-stage breast cancer, glioblastoma, and pancreatic cancer. Each tumor biopsy was analyzed whole-exome sequencing. Founder mutations were selected, accounting for copy number alterations (analyzed using sequenza) and a consensus allele fraction approach that combined pyclone and custom in-house methods. Patient-specific TARDIS primers were designed to detect these mutations in plasma cfDNA. Error suppression in TARDIS was achieved using a combination of unique molecular identifiers and fragment sizes to group sequencing reads into read families. Results: In 33 patients with early-stage breast cancer treated with neoadjuvant therapy, we targeted between 3 and 116 (mean 30) mutations per patient and analyzed between 1 and 4 longitudinal plasma samples using TARDIS. Prior to treatment, we detected ctDNA in 100% patients with Stage I-III breast cancer (n=32, 95% CI= 89%-100%). We detected tumor-specific mutations in 100% of baseline breast cancer plasma samples. After completion of neoadjuvant therapy and before surgery, ctDNA levels were significantly lower in patients with pathologic complete response (pathCR, no evidence of disease at surgery) compared to patients with residual disease (median tumor fractions 0.003% and 0.017%, respectively, p=0.0058, AUC=0.83). Conclusions: TARDIS enables highly sensitive detection of ctDNA in patients with nonmetastatic cancers. Analysis of longitudinal plasma samples using TARDIS holds promise for personalizing the extent of treatment in patients with curable disease. Multiple clinical validation studies across cancer types are ongoing to define quantitative thresholds for changes in ctDNA levels that could improve clinical decision making. Citation Format: Bradon R. McDonald, Tania Contente-Cuomo, Stephen-John Sammut, Michelle D. Stephens, Ahuva Odenheimer-Bergman, Brenda Ernst, Nieves Perdigones, Suet-Feung Chin, Maria Farooq, Rosa Mejia, Patricia A. Cronin, Karen S. Anderson, Heidi E. Kosiorek, Donald W. Northfelt, Ann E. McCullough, Bhavika K. Patel, Jeffrey N. Weitzel, Thomas P. Slavin, Carlos Caldas, Barbara A. Pockaj, Muhammed Murtaza. Personalized monitoring of treatment response using Targeted Digital Sequencing of circulating tumor DNA [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A51.

Background: Recent studies have demonstrated independent validation of the prognostic relevance of residual cancer burden (RCB) after neoadjuvant chemotherapy. However, a pooled subject-level analysis of multiple cohorts is needed to determine estimates of long-term prognosis for each class of RCB in each phenotypic subtype of breast cancer (BC) to better inform on patient outcomes. Also, a pooled subject-level analysis allows more detailed analyses of generalizability of the prognostic meaning of RCB assessments in a broader experience of practice settings. Method: Subject-level RCB results, with relevant clinical and pathologic stage, tumor subtype and grade, demographic, treatment and follow-up data from 11 institutes/trials are being collected for combined analysis. The association between the continuous RCB index and event-free survival (EFS), and distant recurrence free survival (DRFS) were assessed using mixed effect Cox models with the incorporation of random RCB coefficients to account for between-study heterogeneity. We will also allow for differences in baseline hazard across biological BC subtypes and, if needed, across studies as well. In addition to this stratified mixed effect model, a multivariate analysis adjusting for age, T-category, nodal status and grade was performed within each subtype. In addition, mixed effect Cox models will be employed to evaluate association between RCB index with EFS and DRFS within each HR/HER2 subtype. Kaplan Meier estimates of EFS and DRFS at 5 and 10 years were computed for each RCB class within subtype. Results: We analyzed subject-level data from 9 institutes/trials representing 4077 patients currently available from an anticipated final total of 4,800 patients (to be presented at the meeting). There were 950 EFS and 876 DRFS events during follow up (median 65 months, IQR: 70 months). RCB index (continuous) was independently prognostic within each subtype: HR+/HER2- (EFS HR (per unit increase in RCB index) =1.64, 95%CI 1.48-1.82; DRFS HR=1.68, 1.51-1.87), HR+/HER2+ (EFS HR=1.80, 1.57-2.05; DRFS HR=1.93, 1.67-2.24), HR-/HER2+ (EFS HR=2.15, 1.76-2.62; DRFS HR=2.10, 1.77-2.50), and HR-/HER2- (EFS HR=2.05, 1.89-2.22; DRFS HR=2.16, 1.90-2.46); and remained prognostic in multivariate models adjusting for age, grade, and clinical T and N stage at diagnosis. Table 1 contains the response rate and estimated EFS at 5 years and 10 years for each RCB class within each HR/HER2 phenotype (DRFS results were similar). Conclusions: Long-term prognosis after pCR was similarly excellent in all phenotypic subtypes. RCB index and classification was independently and strongly prognostic in all subtypes, and generalizable to multiple practice settings. Prognostic differences by RCB class occurred within 5 years in HR- BC, but extended to 10 years in HR+ BC. RCB-I had slightly worse EFS than pCR in HR- BC and HR+/HER2+ BC (after 5 years), but the same EFS as pCR in HR+/HER2- BC. Complete analysis of all subjects, including neoadjuvant treatments, will be presented at the meeting. PhenotypeOutcomepCRRCB-IRCB-IIRCB-IIIHR+/HER2-Frequency (%)11%10%52%27%(N=1467)5 yr EFS (95% CI)91% (86-96)93% (89-98)82% (79-85)70% (65-75)10 yr EFS (95% CI)84% (75-93)88% (82-95)71% (67-75)52% (46-58)HR+/HER2+Frequency (%)38%18%35%9%(N=762)5 yr EFS (95% CI)94% (91-97)93% (88-98)78% (73-84)49% (37-65)10 yr EFS (95% CI)91% (86-96)79% (70-90)65% (59-73)42% (29-60)HR-/HER2+Frequency (%)66%11%18%5%(N=550)5 yr EFS (95% CI)93% (90-96)88% (79-97)60% (50-71)45% (30-69)10 yr EFS (95% CI)90% (86-94)84% (74-95)56% (46-68)45% (30-69)HR-/HER2-Frequency (%)41%13%33%13%(N=1293)5 yr EFS (95% CI)92% (90-94)85% (79-91)68% (63-72)28% (21-36)10 yr EFS (95% CI)87% (82-91)80% (72-88)63% (58-68)24% (18-33) Citation Format: Christina Yau, Marieke van der Noordaa, Jane Wei, Marie Osdoit, Fabien Reyal, Anne-Sophie Hamy, Marick Lae, Miguel Martin, Maria del Monte, I-SPY 2 TRIAL Consortium, Judy C Boughey, Rebekah Gould, Jelle Wesseling, Tessa Steenbruggen, Maartje van Seijen, Gabe Sonke, Stephen Edge, Stephen-John Sammut, Elena Provenzano, Jean Abraham, Peter Hall, Ashley Graham, Lorna Mackintosh, David Cameron, Alice Wang, Priyanka Sharma, Kimberly Cole, Lajos Pusztai, Mi-Ok Kim, Laura van ‘t Veer, Laura Esserman, W. Fraser Symmans. Residual cancer burden after neoadjuvant therapy and long-term survival outcomes in breast cancer: A multi-center pooled analysis [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr GS5-01.

Accurate detection of minimal residual disease (MRD) can guide individualized management of early stage cancer patients, but current diagnostic approaches lack adequate sensitivity. Circulating tumor DNA (ctDNA) analysis has shown promise for recurrence monitoring but MRD detection immediately after neoadjuvant therapy or surgical resection has remained challenging. We have developed TARgeted DIgital Sequencing (TARDIS) to simultaneously analyze multiple patient-specific cancer mutations in plasma and improve sensitivity for minute quantities of residual tumor DNA. In 77 reference samples at 0.03%-1% mutant allele fraction (AF), we observed 93.5% sensitivity. Using TARDIS, we analyzed ctDNA in 34 samples from 13 patients with stage II/III breast cancer treated with neoadjuvant therapy. Prior to treatment, we detected ctDNA in 12/12 patients at 0.002%-1.04% AF (0.040% median). After completion of neoadjuvant therapy, we detected ctDNA in 7/8 patients with residual disease observed at surgery and in 1/5 patients with pathological complete response (odds ratio, 18.5, Fisher’s exact p=0.032). These results demonstrate high accuracy for a personalized blood test to detect residual disease after neoadjuvant therapy. With additional clinical validation, TARDIS could identify patients with molecular complete response after neoadjuvant therapy who may be candidates for nonoperative management.One Sentence SummaryA personalized ctDNA test achieves high accuracy for residual disease.

Circulating tumour DNA (ctDNA) detection and monitoring has enormous potential clinical utility in oncology. We describe here a fast, flexible and cost-effective method to profile multiple genes simultaneously in low input cell-free DNA (cfDNA): Next Generation-Targeted Amplicon Sequencing (NG-TAS). We designed a panel of 377 amplicons spanning 20 cancer genes and tested the NG-TAS pipeline using cell-free DNA from two hapmap lymphoblastoid cell lines. NG-TAS consistently detected mutations in cfDNA when mutation allele fraction was >1%. We applied NG-TAS to a clinical cohort of metastatic breast cancer patients, demonstrating its potential in monitoring the disease. The computational pipeline is available at: https://github.com/cclab-brca/NGTAS_pipeline.

Pathology archives with linked clinical data are an invaluable resource for translational research, with the limitation that most cancer samples are formalin-fixed paraffin-embedded (FFPE) tissues. Therefore, FFPE tissues are an important resource for genomic profiling studies but are under-utilised due to the low amount and quality of extracted nucleic acids. We profiled the copy number landscape of 356 breast cancer patients using DNA extracted FFPE tissues by shallow whole genome sequencing. We generated a total of 491 sequencing libraries from 2 kits and obtained data from 98.4% of libraries with 86.4% being of good quality. We generated libraries from as low as 3.8ng of input DNA and found that the success was independent of input DNA amount and quality, processing site and age of the fixed tissues. Since copy number alterations (CNA) play a major role in breast cancer, it is imperative that we are able to use FFPE archives and we have shown in this study that sWGS is a robust method to do such profiling.

WES alignment pipelines including either the ICRG, Disambiguate or Xenome - Schematic representation of the WES alignment pipelines developed to compare the ICRG method with Disambiguate and Xenome. (PPTX 41 kb)

The use of circulating DNA(ctDNA) to provide a non-invasive, personalised genomic snapshot of a patients' tumour has huge potential. Over the past five years this area of research has gained huge momentum. A number of studies in metastatic breast cancer have shown the potential of ctDNA to predict prognosis and treatment response using ctDNA. Further developments have included deeper sequencing using whole exome and shallow whole genome approaches which has the potential to identify new mutations and chromosomal copy number changes which appear upon resistance to treatment. In early breast cancer, recent work utilising personalised digital PCR probes has shown huge potential in predicting disease relapse and the detection of micrometastatic disease which could lead to improved treatment and outcome for these patients. Specific pathways of resistance can also be monitored and liquid biopsy approaches for the detection of ESR1 mutations have been used which could identify patients who have become resistant to particular endocrine therapies. The identification of PIK3CA mutations in plasma has also been shown to predict a higher response rate to specific PI3K inhibitors and could be used as a non-invasive screening tool prior to treatment. Further work on the detection of exosomal miRNA and hypermethylated DNA in plasma have shown promise in terms of specificity for early breast cancer detection and could be used to monitor treatment response. This review will focus on technological advances in the field, early detection of relapse and the detection of tumour-specific genomic alterations which could predict treatment response and resistance in patients with breast cancer.

All supplementary figures. (PDF 2333 kb)

The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13–14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13–14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies., Much effort has recently been devoted to understanding the genomics of breast cancer. In this study, the authors integrate somatic mutation data with previously published copy number aberration and gene expression information for nearly 2,500 breast cancer samples.

Invasive lobular carcinoma (ILC) is the second most frequently occurring histological breast cancer subtype after invasive ductal carcinoma (IDC), accounting for around 10% of all breast cancers. The molecular processes that drive the development of ILC are still largely unknown. We have performed a comprehensive genomic, transcriptomic and proteomic analysis of a large ILC patient cohort and present here an integrated molecular portrait of ILC. Mutations in CDH1 and in the PI3K pathway are the most frequent molecular alterations in ILC. We identified two main subtypes of ILCs: (i) an immune related subtype with mRNA up-regulation of PD-L1, PD-1 and CTLA-4 and greater sensitivity to DNA-damaging agents in representative cell line models; (ii) a hormone related subtype, associated with Epithelial to Mesenchymal Transition (EMT) and gain of chromosomes 1q and 8q and loss of chromosome 11q. Using the somatic mutation rate and eIF4B protein level, we identified three groups with different clinical outcomes, including a group with extremely good prognosis. We provide a comprehensive overview of the molecular alterations driving ILC and have explored links with therapy response. This molecular characterization may help to tailor treatment of ILC through the application of specific targeted, chemo- and/or immune-therapies.

A significant proportion of biomedical resources carries information that cross references to anatomical structures across multiple scales. To improve the visualization of such resources in their anatomical context, we developed an automated methodology that produces anatomy schematics in a consistent manner,and provides for the overlay of anatomy-related resource information onto the same diagram. This methodology, called ApiNATOMY, draws upon the topology of ontology graphs to automatically lay out treemaps representing body parts as well as semantic metadata linking to such ontologies. More generally, ApiNATOMY treemaps provide an efficient and manageable way to visualize large biomedical ontologies in a meaningful and consistent manner. In the anatomy domain, such treemaps will allow epidemiologists, clinicians, and biomedical scientists to review, and interact with, anatomically aggregated heterogeneous data and model resources. Such an approach supports the visual identification of functional relations between anatomically colocalized resources that may not be immediately amenable to automation by ontology-based inferencing. We also describe the application of ApiNATOMY schematics to integrate, and add value to, human phenotype-related information—results are found at http://apinatomy.org. The long-term goal for the ApiNATOMY toolkit is to support clinical and scientific graphical user interfaces and dashboards for biomedical resource management and data analytics.

Background: Neutropenic fever in patients receiving chemotherapy is a medical emergency and should be treated promptly within 1 h with antibiotics as specified within the 2009 NCAG report on chemotherapy services. Aim: To determine door-to-assessment, door-to-treatment and door-to-investigation intervals for patients with febrile neutropenia who presented to the inpatient Oncology Ward, the outpatient Oncology Day Unit and the Emergency Department in Addenbrooke's Hospital, Cambridge. Design: Retrospective observational audit. Methods: Thirty-two patients on treatment for solid cancers who were admitted with febrile neutropenia between January and December 2010 were identified, and paper and electronic medical records were analysed to determine door to: assessment, treatment and investigation intervals. Results and conclusions: Patients in this series were assessed quicker and received the first dose of antibiotics faster when they presented to an oncology ward rather than the emergency department. However, imaging was performed faster and blood results issued quicker if performed in the emergency department due to a better infrastructure that has been tailored to comply with national targets. Nonetheless, compliance with optimum standards of care was poor, with only 9% of sampled patients getting antibiotics within 1 h of presenting to hospital, and 53% within 1 h of being assessed by a clinician.

Introduction Breast cancer is a clinically and molecularly heterogeneous disease displaying distinct therapeutic responses. Although recent studies have explored the genomic and transcriptomic landscapes, the epigenetic architecture has received less attention. Material and methods An optimised Reduced Representation Bisulfite Sequencing (RRBS) protocol was used to profile the methylomes of 1482 primary breast tumours (and 237 matched adjacent normal tissues) from the METABRIC cohort. Existing copy number, mRNA/miRNA expression and gene panel mutation data from the same tumours, previously publishedCurtis et al, Nature 2012 was available. Results and discussions Noticeable epigenetic drift (both gain and loss of homogeneous DNA methylation patterns) was observed in breast tumours when compared to normal tissues, with markedly higher differences in late replicating genomic regions. The extent of epigenetic drift was also found to be highly heterogeneous between the breast tumours and was sharply correlated with the tumour’s mitotic index, indicating that epigenetic drift is largely a consequence of accumulation of passive cell division related errors. A novel algorithm called DMARC (Directed Methylation Altered Regions in Cancer) was developed that utilised the tumour-specific drift rates to discriminate between methylation alterations attained as a consequence of stochastic cell division errors (background) and those reflecting a more deterministic biological process (directed). Directed methylation alterations were significantly enriched for gene expression changes in breast cancer, compared to background alterations. By integrating with copy number and mutational profiles for these tumours, mutually exclusive patterns between DNA methylation and genomic aberrations were detected. This led to the identification of potential tumour suppressors and oncogenes, and revealed the deterministic nature of these epigenetic alterations. Finally, intra-tumoural methylation content was analysed to identify tumours associated with low epigenetic polymorphism implying a deterministic process vs. those with high polymorphism reflecting a stochastic process. Intra-tumoural heterogeneity as well as the extent of epiallelic burden were found to be prognostic, and revealed a distinction in the role of epiclonal dynamics in different breast cancer subtypes. Conclusion This constitutes the largest breast cancer methylome study, and has revealed the DNA methylation basis of inter-patient as well as intra-tumour heterogeneity in breast cancer.

Introduction The heterogeneous fates of metastatic breast cancer (MBC) preclude our understanding of both resistance to therapy and escape from cancer immunoediting. Here, we performed a comprehensive molecular analysis of lethal MBC patients (pts), interrogating both the malignant and immune tumour microenvironment (TME) compartments, and T-cell receptor (TCR) repertoires, across multiple metastases (mets). Material and methods Multi-platform profiling of mets (n=182 mets to 22 organs, 5–36 mets/pt), primary tumours (n=6) and ctDNA from body fluids (4.7/pt) in 10 warm autopsies of MBC pts (5 ER+/HER2-, 3 ER+/HER2+, 1 ER-/HER2+, 1 ER-/HER2-), included exome seq (n=86), shallow whole genome seq (n=168), RNA seq (n=61), ultra-deep targeted seq (TS) (n=243), TCRseq (n=70) and IHC (n=102). State-of-the-art bioinformatics was applied to integrate the data. Results and discussions Mutation (mut) burden landscapes varied between pts (11 579 mut, median 255.41 mut/pt) and across mets within each pt (median 122 mut/met; were greater than TCGA mut burden (median 63.5 mut/primary, p=4.927e-14). Landscape of mut and predicted neo-antigen were dominated by stem (present in all mets/pt) or clade (some mets/pt), but not private (one met/pt). TS data confirmed that all primary tumours contained the clonal ancestors of 10 pts, and characterised ctDNA bathing organs. Copy number alteration profiles were remarkably similar across mets in 9 of the 10 pts, except in a ER+/HER2- pt, whose mets shared a common ancestor (1q gain/16q loss), then early sub-clonal evolution occurred. Mets were grouped into phylogenic clades that shared common genomic ancestry and accumulated previously unknown mutation signatures. Mets evolved as communities of clones as a fraction of the metastatic stem and clade mutations were sub-clonal. Immune TME was either homogeneous in a particular metastatic clade, or different across mets to a particular organ. Stem and clade clonotypes prevailed across TCR landscape within each pt. TCR repertoires revealed adaptive immune responses to co-evolve with the metastatic genomes. Conclusion The genomic and immune landscapes demonstrate an unprecedented integrated view of the heterogeneous landscape of genomic aberrations, TME features and T-cell adaptive immune responses in lethal MBC.

1009Background: The heterogeneous fates of MBC preclude our understanding of both resistance to therapy and escape from cancer immunoediting. Here, we performed a comprehensive molecular analysis o...

e14074 Background: The glomerular filtration rate (GFR) is essential for carboplatin chemotherapy dosing, however, the best method to estimate GFR in patients with cancer is unknown. We identify the most accurate and least biased method. Methods: Data on age, sex, height, weight, serum creatinine, and results for GFR from 51Cr-EDTA excretion measurements (51Cr-EDTA GFR) were obtained from Caucasian patients aged 18 years or older with histologically confirmed cancer diagnoses at the University of Cambridge Hospital NHS Trust, UK. We developed a new multivariable linear model for GFR using statistical regression analysis. 51Cr-EDTA GFR was compared to the estimated GFR (eGFR) from seven published and our new model using an internal validation data set and root-mean-squared-error (RMSE) and median residuals. A comparison of carboplatin dosing accuracy based on an absolute percentage error more than 20% (APE > 20%) was undertaken. Results: Between August 2006 and January 2013 data from 2,471 patients were obtained. The new model improved the eGFR accuracy (RMSE 15.00ml/min (95% CI 14.12-16.00)) compared to all published models. Body surface area (BSA) adjusted CKD-EPI was the most accurate published models for eGFR (RMSE 16.30ml/min (95% CI 15.34-17.38)) for the internal validation set. Importantly, the new model reduced the fraction of patients with a carboplatin dose APE > 20% to 14.17% in contrast to 18.62% for BSA adjusted CKD-EPI and 25.51% for the Cockcroft-Gault model. The results were externally validated. Conclusions: In a large data set, from patients with cancer, a new model improves eGFR and carboplatin dose calculations, when compared to BSA adjusted CKD-EPI, the model we identified as the best published model for determination of eGFR in patients with cancer.

Blood cells derive from hematopoietic stem cells through stepwise fating events. To characterize gene expression programs driving lineage choice, we sequenced RNA from eight primary human hematopoietic progenitor populations representing the major myeloid commitment stages and the main lymphoid stage. We identified extensive cell type-specific expression changes: 6711 genes and 10,724 transcripts, enriched in non-protein-coding elements at early stages of differentiation. In addition, we found 7881 novel splice junctions and 2301 differentially used alternative splicing events, enriched in genes involved in regulatory processes. We demonstrated experimentally cell-specific isoform usage, identifying nuclear factor I/B (NFIB) as a regulator of megakaryocyte maturation-the platelet precursor. Our data highlight the complexity of fating events in closely related progenitor populations, the understanding of which is essential for the advancement of transplantation and regenerative medicine. The work described in this manuscript was primarily supported by the European Commission Seventh Framework Program through the BLUEPRINT grant with code HEALTH-F5-2011-282510 (DH, FB, GC, JHAM, KD, LC, MF, SC, SF and SPG). Research in the Ouwehand laboratory is further supported by program grants from the National Institute for Health Research (NIHR, http://www.nihr.ac.uk; to AA, MK, PP, SBGJ, SN, and WHO); and the British Heart Foundation under numbers RP-PG-0310-1002 and RG/09/12/28096 (http://www.bhf.org.uk; to AR and WJA). KF and MK were supported by Marie Curie funding from the NETSIM FP7 program funded by the European Commission. The Cambridge BioResource (http://www.cambridgebioresource.org.uk), the Cell Phenotyping Hub, and the Cambridge Translational GenOmics laboratory (http://www.catgo.org.uk) are supported by an NIHR grant to the Cambridge NIHR Biomedical Research Centre (BRC). Research in the Soranzo laboratory (LV, NS and SW) is further supported by the Wellcome Trust (Grant Codes WT098051 and WT091310) and the EU FP7 EPIGENESYS initiative (Grant Code 257082). Research in the Cvejic laboratory (AC and CL) is funded by the Cancer Research UK under grant number C45041/A14953. SJS is funded by NIHR. MEF is supported by a British Heart Foundation Clinical Research Training Fellowship, number FS/12/27/29405. EBM is supported by a Wellcome Trust grant, number 084183/Z/07/Z. FAC, CL and SW are supported by MRC Clinical Training Fellowships and TB by a British Society of Haematology/NHS Blood and Transplant grant. RJR is a Principal Research Fellow of the Wellcome Trust, grant No. 082961/Z/07/Z. Research in the Flicek laboratory is also supported by the Wellcome Trust (grant number 095908) and EMBL. Research in the Bertone laboratory is supported by EMBL. KF and CvG are supported by FWO-Vlaanderen through grant G.0B17.13N

Nature Communications 7 Article number:11479 (2016); Published: 10 May 2016; Updated: 6 June 2016. The original version of this Article contained an error in the spelling of ‘refine’ in the title of the paper. This has now been corrected in both the PDF and HTML.

Background MammaPrint® is a microarray-based gene expression test cleared by the US Food and Drug Administration to assess recurrence risk in early-stage breast cancer, aimed to guide physicians in making neoadjuvant and adjuvant treatment decisions. The increase in the incidence of invasive lobular carcinomas (ILCs) over the past decades and the modest representation of ILC in the MammaPrint development data set calls for a stratified survival analysis dedicated to this specific subgroup. Study Aim The current study aimed to validate the prognostic value of the MammaPrint test for breast cancer patients with early-stage ILCs. Materials and Methods Univariate and multivariate survival associations for overall survival (OS), distant metastasis-free interval (DMFI), and distant metastasis-free survival (DMFS) were studied in a study population of 217 early-stage ILC breast cancer patients from five different clinical studies. Results and Discussion A significant association between MammaPrint High Risk and poor clinical outcome was shown for OS, DMFI, and DMFS. A subanalysis was performed on the lymph node-negative study population. In the lymph node-negative study population, we report an up to 11 times higher change in the diagnosis of an event in the MammaPrint High Risk group. For DMFI, the reported hazard ratio is 11.1 (95% confidence interval = 2.3–53.0). Conclusion Study results validate MammaPrint as an independent factor for breast cancer patients with early-stage invasive lobular breast cancer. Hazard ratios up to 11 in multivariate analyses emphasize the independent value of MammaPrint, specifically in lymph node-negative ILC breast cancers.